132 research outputs found
A Link Transmission Model with Variable Speed Limits and Turn-Level Queue Transmission at Signalized Intersections
The link transmission model (LTM) is an efficient and widely used macro-level
approach for simulating traffic flow. However, the state-of-the-art LTMs
usually focused on segment-level modelling, in which the traffic operation
differences among multiple turning directions are neglected. Such models are
incapable of differentiating the turn-level queue transmission, resulting in
underrepresented queue spillbacks and misidentification of bottlenecks.
Moreover, a constant free-flow speed is usually assumed to formulate LTMs,
restricting their applications to model dynamic traffic management strategies
involving variable speed limits (VSL) and connected and automated vehicles.
This study proposed an extended LTM with VSL and turn-level queue transmission
to capture the traffic flow propagation at signalized intersections. First,
each road segment (RS) with multiple turning directions is divided into many
free-flow and queueing parts characterized by the triangular fundamental
diagrams. Then, the vehicle propagation within the link is described by the
turn-level link inflow, queue inflow, and outflow, which depends on the
free-flow speed changes. A node model involving an iterative procedure is
further defined to capture the potential queue spillback, which estimates the
actual flow propagation between the adjacent RSs. Simulated and field data were
used to verify the proposed model performance. Results reveal that the proposed
LTM predict traffic operations of complex intersections with multiple turning
movements, VSL and signal control schemes, and enables an accurate yet
computationally tractable representation of flow propagation
A Predictive Spatial Model to Quantify the Risk of Air-Travel-Associated Dengue Importation into the United States and Europe
The number of travel-acquired dengue infections has been on a constant rise in the United States and Europe over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue contributes to the increasing number of dengue cases. This paper reports results from a network-based regression model which uses international passenger travel volumes, travel distances, predictive species distribution models (for the vector species), and infection data to quantify the relative risk of importing travel-acquired dengue infections into the US and Europe from dengue-endemic regions. Given the necessary data, this model can be used to identify optimal locations (origin cities, destination airports, etc.) for dengue surveillance. The model can be extended to other geographical regions and vector-borne diseases, as well as other network-based processes
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